A real-coded genetic algorithm with a direction-based crossover operator

In this paper, we develop a parallel-structured real-coded genetic algorithm (RCGA), named the RGA-RDD, for numerical optimization. Technically, the proposed RGA-RDD integrates three specially designed evolutionary operators – the Ranking Selection (RS), Direction-Based Crossover (DBX), and the Dynamic Random Mutation (DRM) – as a whole to mimic a specific evolutionary process. Unlike the conventional RCGAs that perform evolutionary operators in a series framework, the RGA-RDD embeds a coordinator in the inner parallel loop to organize the operations of the DBX and DRM so that a higher possibility of locating the global optimum is ensured. Besides, based on the results of a systematic parametric analysis, we provide a parameter selection guideline for the settings of the proposed RGA-RDD. Furthermore, a data-driven optimization scheme, which incorporates the uniform design for design of experiments and a shape-tunable neural network for auxiliary decision support, is applied to search for an optimal set of the algorithm parameters. The effectiveness and applicability of the proposed RGA-RDD are demonstrated through a variety of benchmarked optimization problems, followed by comprehensive comparisons with some existing state-of-the-art evolutionary algorithms. Extensive simulation results reveal that the performance of the proposed RGA-RDD is superior to comparative methods in locating the global optimum for real-parameter optimization problems, especially for unsolved multimodal and high-dimensional hybrid functions.